The decoder part of the network learns to generate the desired image based on the features encoded by the encoder.
The decoder part of Poly-GAN, shown in the decoder block of Figure REF , is similar to the decoder used in other GANs, e.g., Cycle GAN [1]}.
The decoder consists of a series of ResNet modules followed by transposed convolution to up-sample the input in each block.
However, there are fewer layers in the decoder which receive information from the encoder through skip connections.
